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#005 GANs – Face editing with Generative Adversarial Networks

04.02.2022

Highlight: Over the past few years in machine learning we’ve seen dramatic progress in the field of generative models. While there are a lot of different flavors of these generative models in this post we want to talk specifically about one model called the Generative Adversarial Network or in short GAN. Have you ever wanted to see what you would look like as part of the opposite gender? Or what about playing with the age…
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#002 GANs – Supervised vs. Unsupervised Learning and Discriminative vs. Generative

19.01.2022

Highlights: GANs and classical Deep Learning methods (classification, object detection) are similar, but they are also fundamentally different in nature. Reviewing their properties will be the topic of this post. Therefore, before we proceed further with the GANs series, it will be useful to refresh and recap what is supervised and unsupervised learning. In addition, we will explain the difference between discriminative and generative models. Finally, we will introduce latent variables, since they are an…
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How did famous tennis players respond to the Djokovic visa saga?

16.01.2022

Sportsmanship in Tennis as revealed by Artificial Intelligence Software.What famous tennis players REALLY think and FEEL? As Sigmund Freud remarked: “No mortal can keep a secret. If his lips are silent, he chatterswith his fingertips; betrayal oozes out of him at every pore.” What do the famous tennis players really think about the number one tennis player Novak Djokovic? Here you are going to find out what Artificial Intelligence says about emotions that tennis players…
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#026 VGGFace: Deep Face Recognition in PyTorch by Oxford VGG

05.01.2022

Highlights: Is your goal to do face recognition in photographs or in videos? This distinguished paper, 2015, Deep Face Recognition proposed a novel solution to this. Although the period was very fruitful with contributions in the Face Recognition area, VGGFace presented novelties that enabled a large number of citations and worldwide recognition. Here, we will present a paper overview and provide a code in PyTorch to implement it.  Overview: Theoretical background VGGFace network architecture VGGFace…
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#025 FaceNet: A Unified Embedding for Face Recognition and Clustering in PyTorch

05.01.2022

Highlights: Face recognition represents an active area of research for more than 3 decades. This paper, FaceNet, published in 2015, introduced a lot of novelties and significantly improved the performance of face recognition, verification, and clustering tasks. Here, we explore this interesting framework that become popular for introducing 1) 128-dimensional face embedding vector and 2) triplet loss function. In addition to the theoretical background, we give an outline of how this network can be implemented…
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The hundred-page Computer Vision book
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Learn how to align Faces in OpenCV in Python

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